Our work addresses the planning, control, and mapping issues for autonomous robot teams that operate in challenging, partially observable, dynamic environments with limited field-of-view sensors. In such scenarios, individual robots need to be able to plan/execute safe paths on short timescales to avoid imminent collisions. Performance can be improved by planning beyond the robots’ immediate sensing horizon using high-level semantic descriptions of the environment. For mapping on longer timescales, the agents must also be able to align and fuse imperfect and partial observations to construct a consistent and unified representation of the environment. Furthermore, these tasks must be done autonomously onboard, which typically adds significant complexity to the system. This talk will highlight three recently developed solutions to these challenges that have been implemented to (1) robustly plan paths and demonstrate high-speed agile flight of a quadrotor in unknown, cluttered environments; and (2) plan beyond the line-of-sight by utilizing the learned context within the local vicinity, with applications in last-mile delivery. We further present a multi-way data association algorithm to correctly synchronize partial and noisy representations and fuse maps acquired by (single or multiple) robots, showcased on a simultaneous localization and mapping (SLAM) application.
